微生物运动高通量表征方法的建立和应用

Prevention of biofilm formation is an important, health-related and multidisciplinary research direction. Twitching motility plays essential and distinctive roles through different stages of biofilm formation, for example, at the early stage, twitching motility facilitates bacteria to adapt to surfaces. And at the later stage, twitching motility is required for mature biofilms to form mushroom-like structures. Therefore, twitching motility has received broadly attentions in recent years. However, the in situ method that can be applied to quantitatively characterize twitching motility during biofilm formation is quite limited. By using a combination of high-throughput data acquisition, automatic image processing, database establishment and big data visualization, here, we propose a novel method that can quantitatively characterize twitching motility in high throughput and in single-cell scale manners. Afterwards, we will systematically investigate how twitching motility responds to stimulation of various external signaling molecules or switches of specific internal signaling transduction pathways during the Pseudomonas aeruginosa biofilm formation. These results will help us to uncover the underlying mechanism by which bacteria deploy their motilities. Finally, we will investigate how these mechanisms affect biofilm formation using modeling and simulations.

细菌生物被膜的防治是关乎人类健康的重要课题，阐明生物被膜形成的机制对其防治具有重大意义。其中，蹭行运动在生物被膜形成过程中对初期细菌适应表面环境以及对后续生物被膜三维结构的形成起决定性作用。因此，蹭行运动的原位表征、量化是生物被膜形成机制研究中的重要科学问题之一，但目前仍缺少成熟完整的方法。针对以上问题，通过集成高通量数据采集、自动化图像处理、数据库建立以及图形化输出等技术，我们拟建立一整套快速、基于单细菌层面的方法实现对蹭行运动的高通量表征。利用所建立的方法，我们将在铜绿假单胞菌生物被膜形成过程中系统地研究、量化多种外源信号分子、胞内信号传导通路对蹭行运动的影响，从而进一步阐明蹭行运动的调节机制。再根据所发现的调节机制建立数学模型，并通过计算模拟的方法揭示蹭型运动在生物被膜形成各个阶段发挥的作用，从而最终为细菌生物被膜的防治奠定理论基础。